OpenRaiser/Intern-Atlas huggingface dataset:
Perceived personality state estimation in dyadic and small group interaction with deep learning methods
Venue: arXiv cs.HC 2022
Year: 2022

Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We find that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-five personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we find that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
Venue: arXiv cs.LG 2022
Year: 2022

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep reinforcement learning (DRL) to learn the best policy for the agent in such an environment without any need to store the huge number of state representations for GSG-I. However, the decision-making process of DRL methods is largely opaque, which results in a lack of trust in their predictions. To tackle this issue, we present an interpretable DRL method for GSG-I that generates visualization to explain the decisions taken by the DRL algorithm. We also show that this approach performs better and works well with a simpler training regimen compared to the existing method.
Piano Learning and Improvisation through Adaptive Visualisation and Digital Augmentation
Venue: arXiv cs.HC 2022
Year: 2022

The task of learning the piano has been a centuries-old challenge for novices, experts and technologists. Several innovations have been introduced to support proper posture, movement, and motivation, while sight-reading and improvisation remain the least-explored areas. In this PhD, we address this gap by redesigning the piano augmentation as an interactive and adaptive space. Specifically, we will explore how to support learners with adaptive visualisations through a two-pronged approach: (1) by designing adaptive visualisations based on the proficiency of the learner to support regular piano playing and (2) by assisting them with expert annotations projected on the piano to encourage improvisation. To this end, we will build a model to understand the complexities of learners' spatiotemporal data and use these to support learning. We will then evaluate our approach through user studies enabling practice and improvisation. Our work contributes to how adaptive visualisations can push music instrument learning and support multi-target selection tasks in immersive spaces.
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
Venue: arXiv cs.CV 2022
Year: 2022

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information that could be used to improve the SR process. On the other hand, followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network, using a metadata insertion block to insert prediction vectors into SR network feature maps. Through comprehensive testing, we prove that state-of-the-art contrastive and iterative prediction schemes can be successfully combined with high-performance SR networks such as RCAN and HAN within our framework. We show that our hybrid models consistently achieve stronger SR performance than both their non-blind and blind counterparts. Furthermore, we demonstrate our framework's robustness by predicting degradations and super-resolving images from a complex pipeline of blurring, noise and compression.
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
Venue: arXiv cs.CV 2022
Year: 2022

Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural networks for various computer vision applications. Specifically, ViT multi-head attention layers make it possible to embed information globally across the overall image. Nevertheless, computing and storing such attention matrices incurs a quadratic cost dependency on the number of patches, limiting its achievable efficiency and scalability and prohibiting more extensive real-world ViT applications on resource-constrained devices. Sparse attention has been shown to be a promising direction for improving hardware acceleration efficiency for NLP models. However, a systematic counterpart approach is still missing for accelerating ViT models. To close the above gap, we propose a first-of-its-kind algorithm-hardware codesigned framework, dubbed ViTALiTy, for boosting the inference efficiency of ViTs. Unlike sparsity-based Transformer accelerators for NLP, ViTALiTy unifies both low-rank and sparse components of the attention in ViTs. At the algorithm level, we approximate the dot-product softmax operation via first-order Taylor attention with row-mean centering as the low-rank component to linearize the cost of attention blocks and further boost the accuracy by incorporating a sparsity-based regularization. At the hardware level, we develop a dedicated accelerator to better leverage the resulting workload and pipeline from ViTALiTy's linear Taylor attention which requires the execution of only the low-rank component, to further boost the hardware efficiency. Extensive experiments and ablation studies validate that ViTALiTy offers boosted end-to-end efficiency (e.g., $3\times$ faster and $3\times$ energy-efficient) under comparable accuracy, with respect to the state-of-the-art solution.
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?
Venue: arXiv cs.CV 2022
Year: 2022

Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods. While the reported improvements are indeed impressive, experiments are mostly limited to face datasets, where the clustered embeddings are highly discriminative or well-separated by class (Recall@1 above 90% and often nearing ceiling), and the experimental methodology seemingly favors the deep methods. We conduct a large-scale empirical study of 17 clustering methods across three datasets and obtain several robust findings. Notably, deep methods are surprisingly fragile for embeddings with more uncertainty, where they match or even perform worse than shallow, heuristic-based methods. When embeddings are highly discriminative, deep methods do outperform the baselines, consistent with past results, but the margin between methods is much smaller than previously reported. We believe our benchmarks broaden the scope of supervised clustering methods beyond the face domain and can serve as a foundation on which these methods could be improved. To enable reproducibility, we include all necessary details in the appendices, and plan to release the code.
Single-Fingered Reconfigurable Robotic Gripper With a Folding Mechanism for Narrow Working Spaces
Venue: arXiv cs.RO 2022
Year: 2022

This letter proposes a novel single-fingered reconfigurable robotic gripper for grasping objects in narrow working spaces. The finger of the developed gripper realizes two configurations, namely, the insertion and grasping modes, using only a single motor. In the insertion mode, the finger assumes a thin shape such that it can insert its tip into a narrow space. The grasping mode of the finger is activated through a folding mechanism. Mode switching can be achieved in two ways: switching the mode actively by a motor, or combining passive rotation of the fingertip through contact with the support surface and active motorized construction of the claw. The latter approach is effective when it is unclear how much finger insertion is required for a specific task. The structure provides a simple control scheme. The performance of the proposed robotic gripper design and control methodology was experimentally evaluated. The minimum width of the insertion space required to grasp an object is 4 mm (1 mm, when using a strategy).
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Venue: arXiv cs.LG 2022
Year: 2022

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.
Sensing and Control of Friction Mode for Contact Area Variable Surfaces (Friction-variable Surface Structure)
Venue: arXiv cs.RO 2022
Year: 2022

Robotic hands with soft surfaces can perform stable grasping, but the high friction of the soft surfaces makes it difficult to release objects, or to perform operations that require sliding. To solve this issue, we previously developed a contact area variable surface (CAVS), whose friction changed according to the load. However, only our fundamental results were previously presented, with detailed analyses not provided. In this study, we first investigated the CAVS friction anisotropy, and demonstrated that the longitudinal direction exhibited a larger ratio of friction change. Next, we proposed a sensible CAVS, capable of providing a variable-friction mechanism, and tested its sensing and control systems in operations requiring switching between sliding and stable-grasping modes. Friction sensing was performed using an embedded camera, and we developed a gripper using the sensible CAVS, considering the CAVS friction anisotropy. In CAVS, the low-friction mode corresponds to a small grasping force, while the high-friction mode corresponds to a greater grasping force. Therefore, by controlling only the friction mode, the gripper mode can be set to either the sliding or stable-grasping mode. Based on this feature, a methodology for controlling the contact mode was constructed. We demonstrated a manipulation involving sliding and stable grasping, and thus verified the efficacy of the developed sensible CAVS.
Certified Numerical Real Root Isolation of Zero-dimensional Multivariate Real Nonlinear Systems
Venue: arXiv cs.CG 2022
Year: 2022

Using the local geometrical properties of a given zero-dimensional square multivariate nonlinear system inside a box, we provide a simple but effective and new criterion for the uniqueness and the existence of a real simple zero of the system inside the box. Based on the result, we design an algorithm based on subdivision and interval arithmetics to isolate all the real zeros of a general real nonlinear system inside a given box. Our method is complete for systems with only finite isolated simple real zeros inside a box. A termination precision is given for general zero-dimensional systems. Multiple zeros of the system are output in bounded boxes. A variety of benchmarks show the effectivity and efficiency of our implementation (in C++). It works for polynomial systems with Bezout bound more than 100 million. It also works for non-polynomial nonlinear systems. We also discuss the limitations of our method.
PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
Venue: arXiv cs.AR 2022
Year: 2022

The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators.
A Reliable and Low Latency Synchronizing Middleware for Co-simulation of a Heterogeneous Multi-Robot Systems
Venue: arXiv cs.RO 2022
Year: 2022

Search and rescue, wildfire monitoring, and flood/hurricane impact assessment are mission-critical services for recent IoT networks. Communication synchronization, dependability, and minimal communication jitter are major simulation and system issues for the time-based physics-based ROS simulator, event-based network-based wireless simulator, and complex dynamics of mobile and heterogeneous IoT devices deployed in actual environments. Simulating a heterogeneous multi-robot system before deployment is difficult due to synchronizing physics (robotics) and network simulators. Due to its master-based architecture, most TCP/IP-based synchronization middlewares use ROS1. A real-time ROS2 architecture with masterless packet discovery synchronizes robotics and wireless network simulations. A velocity-aware Transmission Control Protocol (TCP) technique for ground and aerial robots using Data Distribution Service (DDS) publish-subscribe transport minimizes packet loss, synchronization, transmission, and communication jitters. Gazebo and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP protocols tested our ROS2-based synchronization middleware for packet loss probability and average latency. A thorough ablation research replaced NS-3 with EMANE, a real-time wireless network simulator, and masterless ROS2 with master-based ROS1. Finally, we tested network synchronization and jitter using one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our middleware shows that a large-scale IoT infrastructure with a diverse set of stationary and robotic devices can achieve low-latency communications (12% and 11% reduction in simulation and real) while meeting mission-critical application reliability (10% and 15% packet loss reduction) and high-fidelity requirements.
Learning Visual Representation of Underwater Acoustic Imagery Using Transformer-Based Style Transfer Method
Venue: arXiv cs.CV 2022
Year: 2022

Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a framework for learning the visual representation of underwater acoustic imageries, which takes a transformer-based style transfer model as the main body. It could replace the low-level texture features of optical images with the visual features of underwater acoustic imageries while preserving their raw high-level semantic content. The proposed framework could fully use the rich optical image dataset to generate a pseudo-acoustic image dataset and use it as the initial sample to train the underwater acoustic target recognition model. The experiments select the dual-frequency identification sonar (DIDSON) as the underwater acoustic data source and also take fish, the most common marine creature, as the research subject. Experimental results show that the proposed method could generate high-quality and high-fidelity pseudo-acoustic samples, achieve the purpose of acoustic data enhancement and provide support for the underwater acoustic-optical images domain transfer research.
On the Capacity of "Beam-Pointing" Channels with Block Memory and Feedback: The Binary Case
Venue: arXiv cs.IT 2022
Year: 2022

Millimeter-wave (mmWave) communication is one of the key enablers for 5G systems as it provides larger system bandwidth and the possibility of packing numerous antennas in a small form factor for highly directional communication. In order to materialize the potentially very high beamforming gain, the transmitter and receiver beams need to be aligned. Practically, the Angle-of-Departure (AoD) remains almost constant over numerous consecutive time slots, which presents a state-dependent channel with memory. In addition, the backscatter signal can be modeled as a (causal) generalized feedback. The capacity of such channels with memory is generally an open problem in information theory. Towards solving this difficult problem, we consider a "toy model", consisting of a binary state-dependent (BSD) channel with in-block memory (iBM) [1] and one unit-delayed feedback. The capacity of this model under the peak transmission cost constraint is characterized by an iterative closed-form expression. We propose a capacity-achieving scheme where the transmitted signal carries information and meanwhile uniformly and randomly probes the beams with the help of feedback.
Unsupervised Deep Learning-based clustering for Human Activity Recognition
Venue: arXiv cs.CV 2022
Year: 2022

One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labelled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual information metrics.
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Venue: arXiv cs.LG 2022
Year: 2022

Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles. The main challenge associated with self-learned agents in the form of deep neural networks, is their black-box nature: it is impossible for humans to interpret deep neural networks. Therefore, humans cannot directly interpret the actions of deep neural network based agents, or foresee their robustness in different scenarios. In this work, we demonstrate a method for probing which concepts self-learning agents internalise in the course of their training. For demonstration, we use a chess playing agent in a fast and light environment developed specifically to be suitable for research groups without access to enormous computational resources or machine learning models.
Column Generation for Optimization Problems in Communication Networks
Venue: arXiv cs.DS 2022
Year: 2022

Numerous communication networks are emerging to serve the various demands and improve the quality of service. Heterogeneous users have different requirements on quality metrics such as delay and service efficiency. Besides, the networks are equipped with different types and amounts of resources, and how to efficiently optimize the usage of such limited resources to serve more users is the key issue for communication networks. One powerful mathematical optimization mechanism to solve the above issue is column generation (CG), which can deal with the optimization problems with complicating constraints and block angular structures. In this paper, we first review the preliminaries of CG. Further, the branch-and-price (BP) algorithm is elaborated, which is designed by embedding CG into the branch-and-bound scheme to efficiently obtain the optimal solution. The applications of CG and BP in various communication networks are then provided, such as space-air-ground networks and device-to-device networks. In short, our goal is to help readers refine the applications of the CG optimization tool in terms of problem formulation and solution. We also discuss the possible challenges and prospective directions when applying CG in the communication networks.